Method and system for calculating recoverable reserves of shale gas

Abstract

The invention discloses a shale gas recoverable reserve calculation method and system, and relates to the technical field of shale gas exploitation calculation. The method comprises the following steps of fracturing construction data collection and fracture network three-dimensional modeling, fracture conductivity space distribution inversion, flow simulation and recoverable reserve calculation, inter-well interference effect quantitative correction and closed-loop verification and model parameter optimization. Through three-dimensional crack modeling, flow conductivity dynamic evaluation and inter-well interference correction, and in combination with a closed loop verification mechanism, the accuracy and reliability of recoverable reserve prediction are significantly improved; the invention provides a shale gas recoverable reserve calculation system based on the method, which comprises a data acquisition module, a crack modeling module, a flow simulation module, an inter-well interference correction module and a model verification optimization module, and realizes automation and intelligentization of the whole process from data input to result output. And the accuracy and reliability of shale gas recoverable reserve prediction are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of shale gas exploitation calculation, and particularly to a calculation method and system for shale gas recoverable reserves. Background Art

[0002] As an important unconventional natural gas resource, shale gas is becoming increasingly prominent in the global energy field. With the continuous development of shale gas exploitation technology, its output proportion in energy supply is gradually increasing. Accurately calculating the recoverable reserves of shale gas is crucial for reasonably planning exploitation schemes, evaluating exploitation benefits, and ensuring the stability of energy supply.

[0003] In the field of shale gas exploitation, accurately evaluating the recoverable reserves is the key to achieving efficient development and economic decision-making. When predicting the recoverable reserves of shale gas using traditional methods, it is usually based on simplified geological models and assumptions. One important assumption is that the fracture network has a regular and symmetric shape. This method ignores the actual propagation characteristics of fractures during the fracturing process, especially the non-uniformity of the fracture network. In fact, fracturing fractures in shale reservoirs often exhibit complex asymmetric propagation patterns, which are affected by various factors, including in-situ stress distribution, heterogeneity of rock mechanical properties, and the presence of natural fractures. However, traditional methods fail to fully consider these factors, resulting in a large deviation between the prediction results and the actual exploitation situation. Especially in shale gas development, the complexity of the fracture network has a significant impact on gas flow and exploitation efficiency. Therefore, this defect of the existing technology limits the accurate evaluation of shale gas recoverable reserves.

[0004] In addition, traditional methods also have deficiencies in fracture modeling and conductivity evaluation. Fracture conductivity is one of the key factors affecting shale gas flow and exploitation efficiency, and its distribution in the fracture network is not uniform. However, most of the existing technologies use simplified models to estimate fracture conductivity, ignoring the dynamic changes in proppant concentration in fractures and the spatial distribution characteristics of fracture conductivity. This simplified treatment cannot accurately reflect the changes in fracture conductivity at different positions and times, thereby affecting the simulation accuracy of gas-water two-phase flow. At the same time, when simulating gas-water flow using traditional methods, it is often assumed that the reservoir permeability is uniform, while the presence of fractures in the actual reservoir will lead to anisotropy of permeability, and this difference will also result in inaccurate prediction results. Therefore, the deficiencies of the existing technology in fracture conductivity modeling and flow simulation further exacerbate the deviation of recoverable reserves prediction.

[0005] In a multi-well production environment, the impact of well interference on the recoverable reserves of shale gas is also a factor that cannot be ignored. The production activities between adjacent wells will affect each other through pressure interference, and this interference will change the gas flow path and production efficiency. However, when predicting recoverable reserves using traditional methods, the well interference effect is often ignored, or only simplified empirical formulas are used for correction. This method cannot accurately quantify the degree of influence of well interference on recoverable reserves, resulting in a large difference between the prediction results and the actual production situation. In addition, existing technologies also have deficiencies in model verification and optimization, lacking effective closed-loop verification mechanisms and parameter iterative optimization methods, making it difficult to dynamically adjust the model based on actual production data, and thus unable to ensure the accuracy and reliability of prediction results. Therefore, the deficiencies of existing technologies in considering the well interference effect and model optimization further limit the accurate assessment of shale gas recoverable reserves. Summary of the Invention

[0006] The purpose of the present invention is to solve the problem that the prior art does not consider the influence of non-uniform expansion of fracture networks on recoverable reserves, and the deficiencies in fracture conductivity modeling, flow simulation, quantification of well interference effects, and model optimization in traditional methods, resulting in prediction results deviating from reality, and to propose a calculation method and system for shale gas recoverable reserves.

[0007] The purpose of the present invention can be achieved through the following technical solutions: A calculation method for shale gas recoverable reserves, comprising the following steps: B1: Fracturing construction data collection and three-dimensional modeling of fracture networks. Collect fracturing construction, microseismic, and rock mechanics data through downhole and surface equipment, generate three-dimensional fracture morphology using DFN software, and quantify relevant parameters to provide basic data for subsequent calculations; B2: Inversion of the spatial distribution of fracture conductivity. Collect fracturing flowback fluid samples to determine the proppant concentration, establish a conductivity decay model, and generate a spatial distribution matrix of conductivity to reflect the change in fracture conductivity; B3: Flow simulation and calculation of recoverable reserves under non-uniform fracture constraints. Import fracture parameters and the conductivity matrix into the simulator, set the permeability field, solve the gas-water flow equation, simulate until the economic limit production rate, and obtain the predicted value of the single-well dynamic recoverable reserves; B4: Quantification and correction of well interference effects. Collect pressure data of adjacent wells to calculate the interference velocity and interference coefficient, and correct the predicted value of recoverable reserves to make the result more consistent with the actual production situation; B5: Closed-loop verification and iterative optimization of model parameters. Compare the calculated error between the corrected recoverable reserves and the actual production, adjust the model parameters according to the priority until the error meets the standard, and output the final recoverable reserves and confidence interval.

[0008] Further, the specific process of B1 is as follows: Through downhole sensors and surface monitoring equipment, the fracturing construction data of the target well is collected in real time, specifically including: pumping displacement, sand-fluid ratio, fracturing fluid viscosity, construction pressure curve; Synchronously obtain the microseismic monitoring data and the rock mechanics parameters of the target layer during the fracturing process, input them into the discrete fracture network model software, generate a three-dimensional asymmetric fracture propagation pattern, and quantitatively output the main fracture length , the density of branch fractures and the fracture connectivity index CI.

[0009] Further, the calculation process of the fracture connectivity index CI in B1 is as follows: Classify different fractures, determine the number of main fractures, branch fractures and natural fractures among all fractures, and record them as zs, fs and ff respectively. After normalization, substitute them into the following formula: , where are the preset weight coefficients of the number of main fractures, branch fractures and natural fractures respectively, and are allocated according to their influence degree on the conductivity; is the total fracture length.

[0010] Further, the specific operation steps of B2 are as follows: During the fracturing flowback stage, continuously collect flowback fluid samples at a sampling interval ≤ 2 hours, and measure the concentration distribution data C of the proppant in the flowback fluid at different time periods through an X-ray fluorescence spectrometer i , i represents the i-th fracture, i = 1, 2,..., n; According to the proppant concentration decay law, establish a mathematical model of the conductivity decay coefficient β: ; where, is the conductivity decay coefficient of the m-th fracture, reflecting the decay degree of the conductivity of this fracture over time, and the value range is 0-1; is the initial pumped proppant concentration, is the proppant concentration of the m-th fracture at the flowback time t1, which is obtained by collecting the flowback fluid sample and measuring it with an X-ray fluorescence spectrometer; is the decay rate constant of the m-th fracture, which is obtained by non-linearly fitting the flowback concentration curve; t1 is the flowback time; Map the values of each fracture to the three-dimensional fracture model generated by B1 to form a conductivity spatial distribution matrix [β].

[0011] Further, the specific operation steps of B3 are as follows: Take the fracture parameters of B1, , Import the flow - guiding capacity matrix [β] of CI and B2 into the unstructured grid numerical simulator, and set the reservoir anisotropic permeability field: The permeability K along the main fracture direction x =K base ×(1 + uy1× ), where uy1 = 0.2×(CI / 0.5); the permeability K perpendicular to the fracture direction y =K base ×(1 + uy2× ), where uy2 = 0.05×(CI / 0.5); where Kbase is the matrix permeability, with a range of 0.0001 - 0.001 mD; uy1 and uy2 are dynamic weight coefficients, which are positively correlated with the fracture connectivity index CI; Solve the gas - water two - phase flow control equation: ; where, is the Hamiltonian operator, used to describe the spatial variation of the vector field; is the permeability tensor; is the pressure, referring to the pressure of the gas - water two - phase in the reservoir; is the fluid viscosity; is the porosity, and the porosity is estimated according to the existing regional geological data; is the saturation, including gas saturation and water saturation, which respectively represent the volume ratios of the gas phase and the water phase in the pores, and the sum of the two is 1; is the time variable; Run the simulation until the economic limit production, which is set to 10% of the initial production, and output the predicted value of the single - well dynamic recoverable reserve EUR.

[0012] Furthermore, the specific operation steps of B4 are as follows: Collect the real - time pressure data of adjacent production wells, with the well spacing ranging from 300 to 800 m and the sampling frequency ≥ 1 time / day, and calculate the pressure interference propagation velocity : ; where, is the pressure interference propagation distance; is the propagation time, which is the time required for the pressure interference to propagate from one well to another well; Correct the recoverable reserve: ; where is the corrected recoverable reserve, which is the result obtained by correcting the predicted value of the single - well dynamic recoverable reserve considering the well - to - well interference effect; is the predicted value of the single - well dynamic recoverable reserve, which is the initial recoverable reserve prediction result obtained by simulating the gas - water two - phase flow through the unstructured grid numerical simulator; is the well - to - well interference coefficient, used to quantify the influence degree of the exploitation between adjacent wells on the recoverable reserve of the target well.

[0013] Furthermore, the inter-well interference coefficient is calculated as follows: Through the formula: ; where is the reference pressure propagation velocity, which is a set benchmark value; b is the Arps decline exponent, which is a parameter describing the production decline trend; is the preset standard decline exponent, which is used to standardize the b value; is the weight coefficient, which respectively represents the contribution weights of the pressure propagation velocity and the Arps decline exponent to the interference coefficient, and .

[0014] Furthermore, the specific process of B5 is as follows: Compare the corrected EUR_adj with the actual production data and calculate the absolute error rate: ; where is the absolute error rate, which is used to measure the error degree between the corrected recoverable reserves and the actual cumulative gas production; is the corrected recoverable reserves; is the actual cumulative gas production, which is the total amount of shale gas actually produced by the target well within a certain production time; If Error > 5%, then repeat B1 - B4 after presetting the adjustment priority adjustment parameter: The preset adjustment priority is determined by the formula: ; where is the parameter sensitivity weight, which represents the contribution ratio of a certain parameter to the total error change; is the error change amount caused by the parameter adjustment; is the total error change amount, that is, the sum of the error change amounts generated after adjusting all parameters; The adjusted parameters include: optimizing the fracture length weight factor of the DFN model, with an adjustment range of ±10%; correcting the value in the conductivity attenuation model, with an adjustment range of ±15%; recalibrating the permeability anisotropy coefficient, with an adjustment range of ±5%; When substituting until Error ≤ 5%, output the final recoverable reserves EUR final and the corresponding confidence interval.

[0015] A calculation system for shale gas recoverable reserves includes: A data acquisition module, which is used to collect data on fracturing construction, microseismic monitoring, rock mechanics, flowback fluid proppant concentration, and adjacent well pressure, providing basic information for subsequent calculations; A fracture modeling and analysis module, which is used to construct a fracture model, quantify fracture parameters, establish a conductivity attenuation model, and generate a distribution matrix, providing key parameters for flow simulation; A flow simulation calculation module, which is used to utilize fracture and conductivity data to simulate gas-water flow and calculate the predicted value of the dynamic recoverable reserves of a single well; An inter-well interference correction module, which is used to calculate interference parameters based on the pressure data of adjacent wells, correct the predicted value of recoverable reserves, and improve the calculation accuracy; A model verification and optimization module, which is used to compare the calculation results with actual data, adjust the model parameters until the error meets the standard, and output the reliable final recoverable reserves and confidence interval.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) In the present invention, through the acquisition of fracturing construction data and the three-dimensional modeling of the fracture network, the non-uniform expansion form of fractures is truly restored, the interaction between natural fractures and artificial fractures is considered, and the simplified assumption of the regular and symmetric form of fractures in the traditional method is avoided, thereby significantly improving the accuracy of fracture modeling and the adaptability to actual geological conditions. In addition, in terms of fracture conductivity modeling, by collecting backflow fluid samples and establishing a conductivity attenuation model, the spatial distribution and temporal variation of fracture conductivity can be dynamically reflected, providing more accurate input parameters for gas-water flow simulation and further improving the accuracy of recoverable reserves prediction. It has significant advantages over the prior art in terms of fracture modeling and conductivity evaluation, and can effectively solve the prediction deviation problem caused by the simplification of fracture form in the traditional method; (2) In the present invention, during the process of flow simulation and recoverable reserves calculation, the anisotropic characteristics of reservoir permeability are fully considered, and the permeability field is dynamically adjusted through the fracture connectivity index, making the simulation results closer to the actual gas-water flow situation; at the same time, a quantitative correction module for inter-well interference effect is introduced, which can calculate the interference coefficient according to the pressure data of adjacent wells and correct the predicted value of recoverable reserves, so as to more accurately reflect the actual production situation in the multi-well production environment; compared with the traditional method, through refined flow simulation and inter-well interference correction, the accuracy and reliability of recoverable reserves prediction are significantly improved, providing a more scientific basis for the development decision-making of shale gas fields; (3) The present invention adopts a closed-loop verification and model parameter iterative optimization mechanism. By comparing the corrected recoverable reserves with actual production data and dynamically adjusting the model parameters according to the error situation until the error reaches the preset standard; this optimization mechanism can ensure the adaptability of the model under different geological conditions and development stages, and output the final recoverable reserves and confidence interval. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings; Figure 1 FIG. is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0019] It should be understood that the terms "including" and "comprising" used in the specification and claims of this disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0020] It should also be understood that the terms used in this disclosure specification are only for the purpose of describing specific embodiments and are not intended to limit this disclosure. As used in this disclosure specification and claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in this disclosure specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0021] As Figure 1 shown, a calculation method for recoverable reserves of shale gas includes: Step 1: Fracturing construction data acquisition and three-dimensional modeling of fracture network. Collect fracturing construction, microseismic, and rock mechanics data through downhole and surface equipment, generate three-dimensional fracture morphology using DFN software, and quantify relevant parameters to provide basic data for subsequent calculations. Collect fracturing construction data of the target well in real time through downhole sensors and surface monitoring equipment, specifically including: pumping rate (range 5 - 20 m³ / min), sand-liquid ratio (range 10 - 40%), fracturing fluid viscosity (range 50 - 500 mPa·s), construction pressure curve (sampling frequency ≥ 1 time / second). Simultaneously obtain microseismic monitoring data (location accuracy ≤ 10 m) during the fracturing process and rock mechanics parameters of the target layer (Young's modulus range 10 - 40 GPa, Poisson's ratio range 0.15 - 0.35), input them into the discrete fracture network model (DFN) software (such as FracMan or Petrel), generate three-dimensional asymmetric fracture propagation morphology, and quantify and output the main fracture length and branch fracture density , unit: number of fractures per square meter, and fracture connectivity index CI. The calculation process of the fracture connectivity index CI is as follows: Classify different fractures, determine the number of main fractures, branch fractures and natural fractures among all fractures, and record them as zs, fs and ff respectively. After normalization, substitute them into the following formula: , where are the preset weight coefficients of the number of main fractures, branch fractures and natural fractures respectively, and are allocated according to their influence degrees on the diversion capacity, is the total fracture length.

[0022] Step 2: Inverse inversion of the spatial distribution of fracture diversion capacity. Collect samples of fracturing flowback fluid to measure the proppant concentration, establish a diversion capacity attenuation model, and generate a spatial distribution matrix of diversion capacity to reflect the change of fracture diversion capacity; During the fracturing flowback stage, continuously collect flowback fluid samples (sampling interval ≤ 2 hours), and measure the concentration distribution data C of proppant (particle size range 0.1 - 0.5 mm) in the flowback fluid at different time periods through an X-ray fluorescence spectrometer i (i represents the i-th fracture, i = 1, 2,..., n); According to the proppant concentration attenuation law, establish a mathematical model of the diversion capacity attenuation coefficient β: ; where, is the diversion capacity attenuation coefficient of the m-th fracture, reflecting the attenuation degree of the diversion capacity of this fracture over time, and the value range is 0 - 1; is the initial pumped proppant concentration (unit: kg / m³), is the proppant concentration of the m-th fracture at the flowback time t1 (unit: (kg / m³)), which is obtained by collecting flowback fluid samples and measuring with an X-ray fluorescence spectrometer; is the attenuation rate constant of the m-th fracture (obtained by non-linear regression fitting of the flowback concentration curve), t1 is the flowback time (unit: days); Map the values of each fracture into the three-dimensional fracture model generated in Step 1 to form a spatial distribution matrix of diversion capacity [β].

[0023] Step 3: Flow simulation and recoverable reserve calculation under non-uniform fracture constraints. Import fracture parameters and the diversion capacity matrix into the simulator, set the permeability field, solve the gas-water flow equation, simulate to the economic limit production, and obtain the predicted value of the single-well dynamic recoverable reserve; Import the fracture parameters in Step 1 ( , , CI) and the diversion capacity matrix [β] in Step 2 into an unstructured grid numerical simulator (such as CMG or ECLIPSE), and set the reservoir anisotropic permeability field: The permeability K along the main fracture direction x = K base ×(1 + uy1 × ), uy1 = 0.2×(CI / 0.5); Permeability K in the direction of the vertical fracture y = K base ×(1 + uy2 × ), uy2 = 0.05×(CI / 0.5); Where Kbase is the matrix permeability (range 0.0001 - 0.001 mD); uy1 and uy2 are dynamic weight coefficients, positively correlated with the fracture connectivity index CI; Solve the gas - water two - phase flow control equation: ; Wherein, is the Hamiltonian operator, used to describe the spatial variation of the vector field; is the permeability tensor; is the pressure, referring to the pressure of the gas - water two - phase in the reservoir; is the fluid viscosity, reflecting the internal friction when the gas - water two - phase fluid flows; is the porosity, referring to the proportion of the pore volume in the total volume of the reservoir rock, and the porosity is estimated according to the existing regional geological data; is the saturation, including gas saturation and water saturation, respectively representing the volume proportions of the gas phase and the water phase in the pores, and the sum of the two is 1; is the time variable, used to describe the change of the gas - water two - phase flow process over time; Run the simulation until the economic limit production (set to 10% of the initial production), and output the predicted value of the single - well dynamic recoverable reserve EUR (unit: 100 million cubic meters).

[0024] Step 4: Quantitatively correct the well - to - well interference effect. Collect the pressure data of adjacent wells to calculate the interference velocity and interference coefficient, and correct the predicted value of the recoverable reserve to make the result more in line with the actual production situation; Collect the real - time pressure data (sampling frequency ≥ 1 time / day) of adjacent production wells (well spacing range 300 - 800 m), and calculate the pressure interference propagation velocity Unit: m / day): ; Wherein, is the pressure interference propagation distance (unit: m); is the propagation time (unit: day), which is the time required for the pressure interference to propagate from one well to another well; Correct the recoverable reserve: ; Where is the corrected recoverable reserve (unit: usually (m³)), which is the result obtained by correcting the predicted value of the single - well dynamic recoverable reserve considering the well - to - well interference effect; is the predicted value of the single - well dynamic recoverable reserve (unit: (m³)), which is the initial recoverable reserve prediction result obtained by simulating the gas - water two - phase flow through an unstructured grid numerical simulator; is the interference coefficient between wells, which is used to quantify the influence degree of the exploitation between adjacent wells on the recoverable reserves of the target well; through the formula: ; in the formula is the reference pressure propagation velocity, which is a set benchmark value; b is the Arps decline exponent, which is a parameter describing the production decline trend; is the preset standard decline exponent, which is used to standardize the b value; is the weight coefficient, which respectively represents the contribution weights of the pressure propagation velocity and the Arps decline exponent to the interference coefficient, .

[0025] Step 5, closed-loop verification and iterative optimization of model parameters, compare the calculation error between the corrected recoverable reserves and the actual production, and adjust the model parameters according to the priority until the error meets the standard, and output the final recoverable reserves and confidence interval; Compare the corrected EUR_adj with the actual production data (cumulative gas production, time span ≥ 12 months), and calculate the absolute error rate: ; where is the absolute error rate (%), which is used to measure the error degree between the corrected recoverable reserves and the actual cumulative gas production; is the corrected recoverable reserves; is the actual cumulative gas production (unit: usually m³), which is the total amount of shale gas actually produced by the target well within a certain exploitation time.

[0026] If Error > 5%, then repeat steps 1-4 after adjusting the parameters according to the preset adjustment priority: a. Optimize the fracture length weight factor of the DFN model (adjustment range ±10%); b. Correct the value in the conductivity attenuation model (adjustment range ±15%); c. Re-calibrate the permeability anisotropy coefficient (adjustment range ±5%); The preset adjustment priority is determined by the formula: ; in the formula is the parameter sensitivity weight, which represents the contribution ratio of a certain parameter to the total error change. The larger the weight, the more significant the role of adjusting this parameter in reducing the error; is the error change amount caused by parameter adjustment, that is, when a certain parameter (such as the fracture length weight factor) is adjusted by ±10%, the change amount of the recoverable reserves prediction error; is the total error change amount, that is, the sum of the error change amounts generated after all parameter adjustments; when substituting to Error ≤ 5%, output the final recoverable reserves EUR final and the corresponding confidence interval (P90 - P10).

[0027] A calculation system for recoverable reserves of shale gas, comprising; Data acquisition module: Collect data such as fracturing construction, microseismic monitoring, rock mechanics, concentration of proppant in the flowback fluid, and pressure of adjacent wells, providing basic information for subsequent calculations; Fracture modeling and analysis module: Construct a fracture model, quantify fracture parameters, establish a conductivity attenuation model and generate a distribution matrix, providing key parameters for flow simulation; Flow simulation calculation module: Utilize fracture and conductivity data to simulate gas-water flow and calculate the predicted value of the dynamic recoverable reserves of a single well; Inter-well interference correction module: Calculate interference parameters based on adjacent well pressure data, correct the predicted value of recoverable reserves, and improve the calculation accuracy; Model verification and optimization module: Compare the calculation results with actual data, adjust the model parameters until the error meets the standard, and output the reliable final recoverable reserves and confidence interval.

[0028] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A calculation method for recoverable reserves of shale gas, characterized in that, It includes the following steps: B1: Fracturing construction data collection and three-dimensional modeling of fracture network. Collect fracturing construction, microseismic and rock mechanics data through downhole and surface equipment, generate three-dimensional fracture morphology with DFN software and quantify relevant parameters to provide basic data for subsequent calculations; B2: Inversion of spatial distribution of fracture conductivity. Collect fracturing flowback fluid samples to determine proppant concentration, establish a conductivity attenuation model, and generate a conductivity spatial distribution matrix to reflect the change of fracture conductivity; B3: Flow simulation and calculation of recoverable reserves under the constraint of non-uniform fractures. Import fracture parameters and conductivity matrix into the simulator, set the permeability field, solve the gas-water flow equation, simulate to the economic limit production, and obtain the predicted value of single-well dynamic recoverable reserves; B4: Quantification and correction of inter-well interference effect. Collect adjacent well pressure data to calculate interference velocity and interference coefficient, and correct the predicted value of recoverable reserves to make the result more in line with the actual production situation; B5: Closed-loop verification and iterative optimization of model parameters. Compare the calculated error between the corrected recoverable reserves and the actual production, adjust the model parameters according to the priority until the error meets the standard, and output the final recoverable reserves and confidence interval.

2. The calculation method of shale gas recoverable reserves according to claim 1, characterized in that The specific process of B1 is as follows: Collect fracturing construction data of the target well in real time through downhole sensors and surface monitoring equipment, specifically including: pumping rate, sand-fluid ratio, fracturing fluid viscosity, construction pressure curve; Synchronously obtain microseismic monitoring data and target layer rock mechanics parameters during the fracturing process, input them into the discrete fracture network model software to generate a three-dimensional asymmetric fracture propagation pattern, and quantitatively output the main fracture length , the density of branch fractures and the fracture connectivity index CI.

3. The calculation method of shale gas recoverable reserves according to claim 2, characterized in that The calculation process of the fracture connectivity index CI in B1 is as follows: Classify different cracks, determine the number of main cracks, branch cracks and natural cracks among all cracks, and record them as zs, fs and ff respectively. After normalization, substitute them into the following formula: , where are the preset weight coefficients of the number of main cracks, branch cracks and natural cracks respectively, and are assigned according to their influence degrees on the diversion capacity, is the total crack length.

4. The calculation method of shale gas recoverable reserves according to claim 1, characterized in that The specific operation steps of B2 are as follows: During the fracturing flowback stage, continuously collect flowback fluid samples with a sampling interval ≤ 2 hours, and determine the concentration distribution data C of proppants in the flowback fluid at different time periods through an X-ray fluorescence spectrometer i , where i represents the i-th fracture, and i = 1, 2, …, n; According to the attenuation law of proppant concentration, a mathematical model for the attenuation coefficient β of the conductivity is established: ; where is the attenuation coefficient of the conductivity of the m-th fracture, reflecting the attenuation degree of the conductivity of this fracture with time, and the value range is 0 - 1; is the initial proppant concentration pumped in, is the proppant concentration of the m-th fracture at the flowback time t1, which is obtained by collecting the flowback fluid sample and measuring it with an X-ray fluorescence spectrometer; is the attenuation rate constant of the m-th fracture, which is obtained by non-linearly regressing and fitting the flowback concentration curve; t1 is the flowback time; Map the values of each fracture into the three-dimensional fracture model generated by B1 to form the spatial distribution matrix [β] of the conductivity.

5. The calculation method of shale gas recoverable reserves according to claim 1, characterized in that, The specific operation steps of B3 are as follows: Import the fracture parameters of B1, , and the conductivity matrix [β] of CI and B2 into the unstructured grid numerical simulator, and set the anisotropic permeability field of the reservoir: Permeability K along the main fracture direction x = K base × (1 + uy1 × ), where uy1 = 0.2 × (CI / 0.5); Permeability K perpendicular to the fracture direction y = K base × (1 + uy2 × ), where uy2 = 0.05 × (CI / 0.5); Here, Kbase is the matrix permeability, with a range of 0.0001 - 0.001 mD; uy1 and uy2 are dynamic weight coefficients, which are positively correlated with the fracture connectivity index CI; Solve the gas-water two-phase flow control equations: ; where is the Hamiltonian operator, used to describe the spatial variation of the vector field; is the permeability tensor; is the pressure, referring to the pressure of the gas-water two-phase in the reservoir; is the fluid viscosity; is the porosity, and the porosity is estimated according to the existing regional geological data; is the saturation, including gas saturation and water saturation, respectively representing the volume proportions of the gas phase and the water phase in the pores; is the time variable; Run the simulation to the economic limit production, set it as 10% of the initial production, and output the predicted value EUR of single-well dynamic recoverable reserves.

6. The calculation method of recoverable reserves of shale gas according to claim 1, characterized in that, The specific operation steps of B4 are as follows: Collect real-time pressure data of adjacent production wells with a well spacing range of 300 - 800 m and a sampling frequency of ≥ 1 time / day, and calculate the pressure interference propagation velocity : ; among which, is the pressure interference propagation distance; is the propagation time, which is the time required for the pressure interference to propagate from one well to another well; Revised recoverable reserves: ; where is the revised recoverable reserves, which is the result of revising the predicted value of the dynamic recoverable reserves per well considering the interference effect between wells; is the predicted value of the dynamic recoverable reserves per well, which is the initial predicted result of the recoverable reserves obtained by simulating the gas-water two-phase flow through an unstructured grid numerical simulator; is the interference coefficient between wells, which is used to quantify the influence degree of the exploitation between adjacent wells on the recoverable reserves of the target well.

7. The calculation method of shale gas recoverable reserves according to claim 6, characterized in that, The inter-well interference coefficient is calculated as follows: Through the formula: ; where is the reference pressure propagation velocity, which is a set benchmark value; b is the Arps decline exponent, which is a parameter describing the production decline trend; is the preset standard decline exponent, which is used to standardize the b value; is the weight coefficient, which respectively represents the contribution weights of the pressure propagation velocity and the Arps decline exponent to the interference coefficient, and .

8. The calculation method of shale gas recoverable reserves according to claim 1, characterized in that, The specific process of B5 is as follows: Compare the corrected EUR_adj with the actual production data and calculate the absolute error rate: ; where is the absolute error rate, which is used to measure the error degree between the corrected recoverable reserves and the actual cumulative gas production; is the corrected recoverable reserves; is the actual cumulative gas production; If Error > 5%, repeat B1 - B4 after adjusting the parameters according to the preset adjustment priority: The preset adjustment priority is determined by the formula: wherein is the parameter sensitivity weight, representing the contribution ratio of a certain parameter to the change in the total error; is the error change amount caused by the parameter adjustment; is the total error change amount, that is, the sum of the error change amounts generated after all parameter adjustments; The adjusted parameters include: the fracture length weight factor for optimizing the DFN model, with an adjustment range of ±10%; correcting the value in the conductivity decay model, with an adjustment range of ±15%; recalibrating the permeability anisotropy coefficient, with an adjustment range of ±5%; Output the EUR of the final recoverable reserves when Generation to Error ≤ 5% final and the corresponding confidence interval.

9. A system for calculating the recoverable reserves of shale gas according to any one of claims 1-8, characterized in that, It includes: Data collection module, used to collect fracturing construction, microseismic monitoring, rock mechanics, flowback fluid proppant concentration and adjacent well pressure data to provide basic information for subsequent calculations; Fracture modeling and analysis module, used to construct a fracture model, quantify fracture parameters, establish a conductivity attenuation model and generate a distribution matrix to provide key parameters for flow simulation; Flow simulation calculation module, used to simulate gas-water flow and calculate the predicted value of single-well dynamic recoverable reserves by using fracture and conductivity data; Inter-well interference correction module, used to calculate interference parameters based on adjacent well pressure data, correct the predicted value of recoverable reserves, and improve the calculation accuracy; Model verification and optimization module, used to compare the calculation results with actual data, adjust the model parameters until the error meets the standard, and output reliable final recoverable reserves and confidence interval.

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